@incollection{SmaJanGro1992, Author = {Smagt, Patrick van der and Jansen, A. and Groen, Frans}, Title = {Interpolative robot control with the nested network approach}, Year = {1992}, Pages = {475-480}, Month = {Aug.}, Publisher = {IEEE}, Address = {Glasgow, Scotland, U.K.}, Booktitle = {IEEE Int. Symposium on Intelligent Control}, Keywords = {brml machine-learning}, Abstract = {In the realm of pick-and-place problems, we aim at designing highly adaptive controllers which require minimum knowledge of the manipulator and its sensors. In this area, several models have proven more or less successful in one area or another. Non-neural parameter estimation techniques have been investigated, but real-time computational requirements grow out of bound when the number of state variables increases. The use of a single feed-forward network trained with conjugate gradient back-propagation gives fast and highly adaptive approximation, but needs up to ten feedback steps to get high-precision results. Kohonen networks give a precision up to 0.5cm.~with only two steps, but need thousands of iterations to attain reasonable results. Instead, we introduce the nested network method based on search trees which adapts in real-time and reaches a grasping precision of up to 1mm.~in only three steps.} } @COMMENT{Bibtex file generated on 2018-10-9 with typo3 si_bibtex plugin. Data from https://brml.org/projects/machine-learning-ml/ }